Visualization is one of the most important tools for data science.

It is also a great way to start learning R; when you visualize data, you get an immediate payoff that will keep you motivated as you learn. Afterall, learning a new language can be hard!

This tutorial will teach you how to visualize data with R’s most popular visualization package, ggplot2.

The tutorial focuses on three basic skills:

1. How to create graphs with a reusable template
2. How to add variables to a graph with aesthetics
3. How to make different “types” of graphs with geoms

In this tutorial, we will use the core tidyverse packages, including ggplot2. I’ve already loaded the packages for you, so let’s begin!

These examples are excerpted from R for Data Science by Hadley Wickham and Garrett Grolemund, published by O’Reilly Media, Inc., 2016, ISBN: 9781491910399. You can purchase the book at shop.oreilly.com.

Let’s begin with a question to explore.

mpg

You can test your hypothesis with the mpg dataset that comes in the ggplot2 package. mpg contains observations collected on 38 models of cars by the US Environmental Protection Agency.

To see the mpg data frame, type mpg in the code block below and click “Submit Answer”.

mpg

You can use the black triangle that appears at the top right of the table to scroll through all of the columns in mpg.

Among the variables in mpg are:

1. displ, a car’s engine size, in liters.
2. hwy, a car’s fuel efficiency on the highway, in miles per gallon (mpg). A car with a low mpg consumes more fuel than a car with a high mpg when they travel the same distance.

Now let’s use this data to make our first graph.

A plot

The code below uses functions from the ggplot2 package to plot the relationship between displ and hwy.

To see the plot, click “Run Code.”

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Can you spot the relationship?

And the answer is…

The plot shows a negative relationship between engine size (displ) and fuel efficiency (hwy). Points that have a large value of displ have a small value of hwy and vice versa.

In other words, cars with big engines use more fuel. If that was your hypothesis, you were right!

Now let’s look at how we made the plot.

ggplot()

Here’s the code that we used to make the plot. Notice that it contains three functions: ggplot(), geom_point(), and aes().

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

In R, a function is a name followed by a set of parentheses. Many functions require special information to do their jobs, and you write this information between the parentheses.

ggplot

The first function, ggplot(), creates a coordinate system that you can add layers to. The first argument of ggplot() is the dataset to use in the graph.

By itself, ggplot(data = mpg) creates an empty graph, which looks like this.

ggplot(data = mpg)

geom_point()

geom_point() adds a layer of points to the empty plot created by ggplot(). This gives us a scatterplot.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

mapping = aes()

geom_point() takes a mapping argument that defines which variables in your dataset are mapped to which axes in your graph. The mapping argument is always paired with the function aes(), which you use to gather together all of the mappings that you want to create.

Here, we want to map the displ variable to the x axis and the hwy variable to the y axis, so we add x = displ and y = hwy inside of aes() (and we separate them with a comma).

Where will ggplot2 look for these mapped variables? In the data frame that we passed to the data argument, in this case, mpg.

A graphing workflow

Our code follows the common workflow for making graphs with ggplot2. To make a graph, you:

1. Start the graph with ggplot()
2. Add elements to the graph with a geom_ function
3. Select variables with the mapping = aes() argument

A graphing template

In fact, you can turn our code into a reusable template for making graphs. To make a graph, replace the bracketed sections in the code below with a data set, a geom_ function, or a collection of mappings.

Give it a try! Replace the bracketed sections with mpg, geom_boxplot, and x = class, y = hwy to make a slightly different graph. Be sure to delete the # symbols before you run the code.

# ggplot(data = <DATA>) + 
#  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

ggplot(data = mpg) + 
  geom_boxplot(mapping = aes(x = class, y = hwy))

Common problems

As you start to run R code, you’re likely to run into problems. Don’t worry — it happens to everyone. I have been writing R code for years, and every day I still write code that doesn’t work!

Start by carefully comparing the code that you’re running to the code in the examples. R is extremely picky, and a misplaced character can make all the difference. Make sure that every ( is matched with a ) and every " is paired with another ". Also pay attention to capitalization; R is case sensitive.

+ location

One common problem when creating ggplot2 graphics is to put the + in the wrong place: it has to come at the end of a line, not the start. In other words, make sure you haven’t accidentally written code like this:

ggplot(data = mpg) 
+ geom_point(mapping = aes(x = displ, y = hwy))

help

If you’re still stuck, try the help. You can get help about any R function by running ?function_name in a code chunk, e.g. ?geom_point. Don’t worry if the help doesn’t seem that helpful — instead skip down to the bottom of the help page and look for a code example that matches what you’re trying to do.

If that doesn’t help, carefully read the error message that appears when you run your (non-working) code. Sometimes the answer will be buried there! But when you’re new to R, you might not yet know how to understand the error message. Another great tool is Google: try googling the error message, as it’s likely someone else has had the same problem, and has gotten help online.

Exercise 1

Run ggplot(data = mpg) what do you see?

Exercise 2

Make a scatterplot of cty vs hwy.

"Scatterplots are also called points plots and bubble plots. They use the point geom."

Exercise 3

What happens if you make a scatterplot of class vs drv. Try it. Why is the plot not useful?

A closer look

In the plot below, one group of points (highlighted in red) seems to fall outside of the linear trend between engine size and gas mileage. These cars have a higher mileage than you might expect. How can you explain these cars?

A hypothesis

Let’s hypothesize that the cars are hybrids. One way to test this hypothesis is to look at the class value for each car. The class variable of the mpg dataset classifies cars into groups such as compact, midsize, and SUV. If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and SUVs became popular). To check this, we need to add the class variable to the plot.

Aesthetics

You can add a third variable, like class, to a two dimensional scatterplot by mapping it to a new aesthetic. An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points.

You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word “value” to describe data, let’s use the word “level” to describe aesthetic properties. Here we change the levels of a point’s size, shape, and color to make the point small, triangular, or blue:

A strategy

We can add the class variable to the plot by mapping the levels of an aesthetic (like color) to the values of class. For example, we can color a point green if it belongs to the compact class, blue if it belongs to the midsize class, and so on.

Let’s give this a try. Fill in the blank piece of code below with color = class. What happens? Delete the commenting symbols (#) before running your code. (If you prefer British English, you can use colour instead of color.)

# ggplot(data = mpg) + 
#   geom_point(mapping = aes(x = displ, y = hwy, ____________))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

And the answer is…

The colors reveal that many of the unusual points in mpg are two-seater cars. These cars don’t seem like hybrids, and are, in fact, sports cars! Sports cars have large engines like SUVs and pickup trucks, but small bodies like midsize and compact cars, which improves their gas mileage. In hindsight, these cars were unlikely to be hybrids since they have large engines.

This isn’t the only insight we’ve gleaned; you’ve also learned how to add new aesthetics to your graph. Let’s review the process.

Aesthetic mappings

To map an aesthetic to a variable, set the name of the aesthetic equal to the name of the variable, and do this inside mapping = aes(). ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable. ggplot2 will also add a legend that explains which levels correspond to which values.

This insight gives us a new way to think about the mapping argument. Mappings tell ggplot2 more than which variables to put on which axes, they tell ggplot2 which variables to map to which visual properties. The x and y locations of each point are just two of the many visual properties displayed by a point.

Other aesthetics

In the above example, we mapped color to class, but we could have mapped size to class in the same way.

Change the code below to map size to class. What happens?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))

alpha

You can also map class to the alpha aesthetic, which controls the transparency of the points. Try it below.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

Shape

Let’s try one more aesthetic. This time map the class of the points to shape, then look for the SUVs. What happened?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

Exercise 1

In the code below, map cty, which is a continuous variable, to color, size, and shape. How do these aesthetics behave differently for continuous variables, like cty, vs. categorical variables, like class?

# Map cty to color
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# Map cty to size
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# Map cty to shape
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Exercise 2

Map class to color, size, and shape all in the same plot. Does it work?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Exercise 3

What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)? Try it.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Setting aesthetics

What if you just want to make all of the points in your plot blue, like in the plot below?

You can do this by setting the color aesthetic outside of the aes() function, like this

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Setting vs. Mapping

Setting works for every aesthetic in ggplot2. If you want to manually set the aesthetic to a value in the visual space, set the aesthetic outside of aes().

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue", shape = 3, alpha = 0.5)

If you want to map the aesthetic to a variable in the data space, map the aesthetic inside aes().

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class, shape = fl, alpha = displ))

Exercise 4

What do you think went wrong in the code below? Fix the code so it does something sensible.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Recap

For each aesthetic, you associate the name of the aesthetic with a variable to display, and you do this within aes().

Once you map a variable to an aesthetic, ggplot2 takes care of the rest. It selects a reasonable scale to use with the aesthetic, and it constructs a legend that explains the mapping between levels and values. For x and y aesthetics, ggplot2 does not create a legend, but it creates an axis line with tick marks and a label. The axis line acts as a legend; it explains the mapping between locations and values.

You’ve experimented with the most common aesthetics for points: x, y, color, size, alpha and shape. Each geom uses its own set of aesthetics (you wouldn’t expect a line to have a shape, for example). To find out which aesthetics a geom uses, open its help page, e.g. ?geom_line.

This raises a new question that we’ve only brushed over: what is a geom?

Geoms

How are these two plots similar?

Both plots contain the same x variable, the same y variable, and both describe the same data. But the plots are not identical. Each plot uses a different visual object to represent the data. In ggplot2 syntax, we say that they use different geoms.

A geom is the geometrical object that a plot uses to represent observations. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom.

As we see above, you can use different geoms to plot the same data. The plot on the left uses the point geom, and the plot on the right uses the smooth geom, a smooth line fitted to the data.

Geom functions

To change the geom in your plot, change the geom function that you add to ggplot(). For example, take this code which makes the plot on the left (above), and change geom_point() to geom_smooth(). What do you get?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# right
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

More about geoms

ggplot2 provides over 30 geom functions that you can use to make plots, and extension packages provide even more (see https://exts.ggplot2.tidyverse.org/gallery/ for a sampling). You’ll learn how to use these geoms to explore data in the Visualize Data primer.

Until then, the best way to get a comprehensive overview of the available geoms is with the ggplot2 cheatsheet. To learn more about any single geom, look at its help page, e.g. ?geom_smooth.

Exercise 1

What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

Exercise 2

Putting it all together

The ideas that you’ve learned here: geoms, aesthetics, and the implied existence of a data space and a visual space combine to form a system known as the Grammar of Graphics.

The Grammar of Graphics provides a systematic way to build any graph, and it underlies the ggplot2 package. In fact, the first two letters of ggplot2 stand for “Grammar of Graphics”.

The Grammar of Graphics

The best way to understand the Grammar of Graphics is to see it explained in action:

What is a package?

Throughout this tutorial, I’ve referred to ggplot2 as a package. What does that mean?

The R language is subdivided into packages, small collections of data sets and functions that all focus on a single task. The functions that we used in this tutorial come from one of those packages, the ggplot2 package, which focuses on visualizing data.

What should you know about packages?

When you first install R, you get a small collection of core packages known as base R. The remaining packages—there are over 10,000 of them—are optional. You don’t need to install them unless you want to use them.

ggplot2 is one of these optionals packages, so are the other packages that we will look at in these tutorials. Some of the most popular and most modern parts of R come in the optional packages.

You don’t need to worry about installing packages in these tutorials. Each tutorial comes with all of the packages that you need pre-installed; this is how we make the tutorials easy to use.

However, one day, you may want to use R outside of these tutorials. When that day comes, you’ll want to remember which packages to download to acquire the functions you use here. Throughout the tutorials, I will try to make it as clear as possible where each function comes from!

If you’d like to learn more about installing R packages (or R or the RStudio IDE), the Set Up video tutorial walks you through the process of setting up R on your own computer.

Where to from here

Congratulations! You can use the ggplot2 code template to plot any dataset in many different ways. As you begin exploring data, you should incorporate these tools into your workflow.

There is much more to ggplot2 and Data Visualization than we have covered here. If you would like to learn more about visualizing data with ggplot2, check out RStudio’s primer on Data Visualization.

Your new data visualization skills will make it easier to learn other parts of R, because you can now visualize the results of any change that you make to data. you’ll put these skills to immediate use in the next tutorial, which will show you how to extract values from datasets, as well as how to compute new variables and summary statistics from your data. See you there.